Extracting Signals From Noise

  |  May 10, 2010   |  Comments

At the eMetrics conference, analytics experts examined how to get better insights from an increasing amount of data.

"Signals from noise." That phrase kept popping up during the three-day eMetrics Marketing Optimization Summit in San Jose, CA, last week.

I used this phrase during my presentation to highlight the challenges that we all face in business as we process an enormous amount of data available to us at the moment. The Web is a very noisy place; the data it generates and the data that we collect are also very noisy. Our role as analysts is to extract signals from the noise, to interpret those signals, and to produce actionable insights. Yet, that job becomes tougher as the data landscape gets more complex.

A core thrust of the conference was around social media measurement; it's fast becoming a discipline in its own right.

Jim Sterne, the summit's founder, also talked about signals from noise in his opening keynote presentation on social media metrics and about the need to start to attach a value to social media activity.

As the data landscape becomes more complex, different tools are needed to collect and analyze these different types of data.

Avinash Kaushik, Google's analytics evangelist, used his keynote presentation to take us through a variety of different tools to help us grapple with the complexities of long-tail and social media analysis and also reminded us that the goal is to ensure we are measuring outcomes and not just activity. Scott Calise, senior manager of digital research at MTV Networks, showed us a way to develop a coherent approach to social media measurement that includes being clear about the business objectives and putting the right processes in place to track the impacts. Perhaps nothing radically new there but lots of people were taking notes!

This year there was also an increased emphasis on more advanced analytical techniques. I had the opportunity to talk about the use of data mining and predictive analytical techniques in the digital space in areas such as visitor segmentation and propensity modelling. Other sessions I attended dealt with issues such as simulation techniques to understand long-term trends and the thorny issue of campaign attribution. It was good to see some of these classic marketing analysis techniques gaining traction in the online world.

Extracting signals from noise is not just about the use of different tools or the application of more advanced analytical techniques. It can also just be about effective presentation. The session, "Behavioral Analysis and Use Case Analysis in Usability Studies," by SEMphonic CEO Gary Angel and Kohler Web Analyst Russ Rueden took us through their approach to the way that information is presented internally to others at Kohler. Use-case analysis provides the structure for analysis and problem solving. But I was also struck by the simple and coherent presentation framework they use to communicate results to Kohler's team. Generally, they use a total of one to two slides to outline the problem, provide supporting evidence and analysis, offer specific recommendations, and then show real-world examples of those recommendations in action on other sites. According to Angel and Rueden, this last part is important because it allows executives to visualize what the recommendations actually mean for their organization.

This type of information visualization is a key to obtaining signals from noise. So it wasn't surprising that eMetrics' panel discussion on marketing dashboards was standing room only. Interestingly there wasn't as much discussion as I might have expected about the actual technologies for dashboard production. Instead the discussion examined what to actually put into dashboards and then how to get people to take action as a result. The conclusion: you need to ensure that the signals on the dashboard are the right signals and that they are pushed to the right people in the organization in the right way. The person producing the dashboard must fully understand the company's business requirements and assure they are reflected in the dashboard, using whatever is the most appropriate technology.

Dashboards benefit enormously from added insight and that means some additional value being delivered by the analyst, according to Angel and Rueden. This brings me back to my first point about the role of analysts is to extract signals from noise, not just to report the noise.

Additionally, the Web Analytics Association (WAA) held its first certification sessions at the conference and so we may well soon have our first WAA certified web analyst (CWA). One very experienced analyst that I spoke to who took the test said that it certainly made her think and that the most challenging part was the case study analysis. Again, signals from noise.

Finally, I'd just like to say that it was great to meet some of you who read this column in San Jose. I appreciate your comments and feedback.

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ABOUT THE AUTHOR

Neil Mason

Neil Mason is SVP, Customer Engagement at iJento. He is responsible for providing iJento clients with the most valuable customer insights and business benefits from iJento's digital and multichannel customer intelligence solutions.

Neil has been at the forefront of marketing analytics for over 25 years. Prior to joining iJento, Neil was Consultancy Director at Foviance, the UK's leading user experience and analytics consultancy, heading up the user experience design, research, and digital analytics practices. For the last 12 years Neil has worked predominantly in digital channels both as a marketer and as a consultant, combining a strong blend of commercial and technical understanding in the application of consumer insight to help major brands improve digital marketing performance. During this time he also served as a Director of the Web Analytics Association (DAA) for two years and currently serves as a Director Emeritus of the DAA. Neil is also a frequent speaker at conferences and events.

Neil's expertise ranges from advanced analytical techniques such as segmentation, predictive analytics, and modelling through to quantitative and qualitative customer research. Neil has a BA in Engineering from Cambridge University and an MBA and a postgraduate diploma in business and economic forecasting.

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